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NASA NTRS2017

Volatile Content of 4-Vesta: Evidence from Unequilibrated Eucrites

Eucrites are a class of basaltic meteorites that, along with the howardites and diogenites, likely derive from the asteroid 4-Vesta. This asteroid is depleted in moderately volatile elements relative to the Earth and carbonaceous chondrites. Extrapolation of this depletion trend predicts that bulk silicate 4-Vesta (BSV) contains at most 250-1000 μg/g H2O, which is approximately a factor of two lower than the H2O content of Earth. To obtain more accurate H2O and F estimates for BSV, we examined four unequilibrated antarctic meteorites, Yamato(Y)-793548, Y-82210, Y-75011, and Y-74450, by EPMA and SIMS. Pyroxenes contain MgO-rich cores and FeO-rich rims, consistent with primary magmatic zoning. Volatile concentrations generally follow patterns expected for growth zoning with lower values in the cores and higher in the rims. These features indicate that thermal metamorphism and other post-crystallization processes did not significantly perturb the volatile contents of these unequilibrated eucrite pyroxenes. We used these data to derive best estimates for the BSV H2O and F content based on experimentally determined pyroxene-melt partition coefficients and models for magma generation on Vesta. In addition, we measured D/H in the early crystallizing pyroxenes and late crystallzing apatites. We find that the D/H of pyroxene and apatite are within error of one another as well as previous measurements of apatite in equilibrated eucrites. These results imply that degassing was minimal or did not fractionate D/H. Degassing may have been limited if eucrites were shallowly emplaced sills or dykes, or the total H2O content of the magmas was too low for vapor saturation. An alternative mechanism for limited D/H fractionation is that degassing did occur, but the H2/H2O of the exsolved vapor was approximately 15:85, as predicted from experiments.

Sarafian, A. R.↗

NASA NTRS2018

Commercial Off-The-Shelf GPU Qualification for Space Applications

With increased sensor data rates, and limited downlink capability, NASA missions have increased demands for onboard processing for applications ranging from synthetic aperture radar (SAR) data reduction to hyperspectral image processing and recognition, and even artificial intelligence (AI). Graphics Processor Units (GPUs) offer an attractive processing architecture for many of the applications due to their massive parallelism. As no radiation hardened GPU devices currently exist, any near term GPU-based onboard processors must use commercially available devices. To address this need NASA GSFC is collaborating with Cubic Aerospace Incorporated to, (a) characterize the capability of GPUs to meet the demands of a candidate onboard processing application, thereby demonstrating their ability to improve mission performance, reduce spacecraft SWaP, and potentially enable new missions, and (b) evaluate the radiation tolerance of capable COTS GPU devices to determine their suitability for spaceflight applications and understand any mitigations that are needed. A candidate onboard processing image has been prototyped and evaluated on a commercial GPU board and has demonstrated significantly increased processing throughput. Radiation tests for commercial GPU devices are planned for early fiscal year 2019.

Onboard processing↗

NASA NTRS2003

Space Environment Factors Affecting the Performance of International Space Station Materials: The First Two Years of Flight Operations

In this paper, the natural and induced space environment factors affecting materials performance on ISS are described in some detail. The emphasis will be on ISS flight experience and the more significant design and development issues of the last two years. The intent is to identify and document the set of space environment factors, affecting materials, that are producing the largest impacts on the ISS flight hardware verification and acceptance process and on ISS flight operations. Orbital inclination (S1.6 ) and altitude (nominal3S0 km to 400 km altitude) determine the set of natural environment factors affecting the functional life of materials and subsystems on ISS. ISS operates in the F2 region of Earth's ionosphere in well-defined fluxes of atomic oxygen, other ionospheric plasma species, and solar UV, VUV, and x-ray radiation, as well as galactic cosmic rays, trapped radiation, and solar cosmic rays (1,2). The high latitude orbital environment also exposes external surfaces to significantly less well-defined or predictable fluxes of higher energy trapped electrons and auroral electrons (3 ,4). The micrometeoroid and orbital debris environment is an important determinant of spacecraft design and operations in any orbital inclination. Environment factors induced by ISS flight operations include ram-wake effects, magnetic induction voltages arising from flight through Earth's magnetic field, hypergolic thruster plume impingement from proximity operations of visiting vehicles, materials outgassing, venting and dumping of fluids, ISS thruster operations, as well as specific electrical power system interactions with the ionospheric plasma (S-7). ISS must fly in a very limited number of approved flight attitudes leading to location specific environmental exposures and extreme local thermal environments (8). ISS is a large vehicle and produces a deep wake structure from which both ionospheric plasma and neutrals (atomic oxygen) are largely excluded (9-11). At high latitude, the ISS wake may produce a spacecraft charging environment similar to that experienced by the DMSP and Freja satellites (800 to 100 km altitude polar orbits), especially during geo-magnetic disturbances (12-14). ISS is also subject to magnetic induction voltages (VxB L) on conducting structure, a result of high velocity flight through Earth's magnetic field. The magnitude of the magnetic induction voltage varies with location on ISS, as well as the relative orientation of the vehicle velocity vector and planetary magnetic field vector, leading to maximum induction voltages at high latitude (15). The space environment factors, natural and induced, that have had the largest impact on pre-launch ISS flight hardware verification and flight operations during the first two years of ISS flight operations are listed below and grouped according to the physical and chemical processes driving their interaction with ISS materials.

Koontz, Steven L.↗

NASA NTRS2018

Comparison of Test Methods to Determine Failure Parameters for MAT162 Calibration

MAT162, a laminated composite failure material model developed by The Material Sciences Corporation for the commercial finite element software LS-Dyna, is widely used within the aerospace industry to predict damage events under a range of dynamic conditions. The material model involves numerous inputs consisting of both physical material properties and numerical calibration parameters. Due to the large number material card inputs, often there is a lack of uniqueness to MAT162 material cards that limits the predictive capability to only the directly calibrated space. To expand this space, MAT162 requires a prudent and robust calibration process in which significant parameters are calibrated to high confidence damage events observed in experiment. Critical to this success is fully defining the material properties correctly, namely the fiber crush (SFC) and fiber shear (SFS) values, prior to calibrating the numerical parameters. In this paper, the effect of the determination of SFS and SFC on subsequent calibration steps is examined using two different experimental techniques.

Molitor, Matthew↗

DOE OSTI2024

Development of an Attenuated Total Reflectance–Ultraviolet–Visible Probe for the Online Monitoring of Dark Solutions

Optical spectroscopy is a valuable tool for on-line monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy in particular, can monitor the concentration of analytes as well as identify speciation and oxidation state. However, it can be difficult to impossible to employ UV-vis based sensors on chemical systems that are very dark (i.e., high optical density) as exceedingly short pathlengths are required (for transmission approaches) or effective means of backscattering are needed (for reflectance approaches). Examples of processes that would benefit significantly from the use of optical sensors and encounter these challenges include used nuclear fuel recycling and molten salts with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for varied processes can be time consuming and expensive. Here, in this study, we evaluate the ability for a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible “pathlengths” by altering the exposed ATR fiber length. Also studied were approaches to loading and measuring the sensor cell. These results are compared to a traditional 1 cm cuvette measured by transmission UV-vis. It was found that the ATR-UV-vis probe was capable of measuring solutions 600 times more concentrated than the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the interrogation of dark solutions is a promising avenue for use in on-line monitoring of nuclear processes.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

DISSOLUTION OF SURROGATE U-Zr FUEL USING ALNIFLEX CONDITIONS

Non-aluminum clad spent nuclear fuel (NASNF) stored in L Basin at the Savannah River Site (SRS) is widely varied in fuel composition, design, packaging, and physical condition. The complexity of the NASNF inventory presents significant challenges, and technology development is necessary for successful disposition. One such fuel in the inventory is metallic uranium-zirconium (U-Zr) alloy fuel, the focus of this study. Electrolytic or nitric acid only dissolution of metallic U-Zr alloy can form insoluble zirconium oxide, which results in up to 52% loss of U to insoluble solids, and can be subject to potentially uncontrolled oxidation reactions [1, 2]. The AlNiflex process was determined to be a viable dissolution flowsheet for the U-Zr alloy fuel. Under a narrow set of solution concentrations, a combination of hydrofluoric acid (HF), nitric acid (HNO3), aluminum nitrate (Al(NO3)3), and hexavalent chromium can safely dissolve U-Zr intermetallic alloys, keep Zr soluble, and not significantly corrode stainless steel (SS) vessels [3, 4.

Gogolski, Jarrod M. [Savannah River National Labor↗

NASA NTRS2010

Wind Tunnel Investigation of Ground Wind Loads for Ares Launch Vehicle

A three year program was conducted at the NASA Langley Research Center (LaRC) Aeroelasticity Branch (AB) and Transonic Dynamics Tunnel (TDT) with the primary objective to acquire scaled steady and dynamic ground-wind loads (GWL) wind-tunnel data for rollout, on-pad stay, and on-pad launch configurations for the Ares I-X Flight Test Vehicle (FTV). The experimental effort was conducted to obtain an understanding of the coupling of aerodynamic and structural characteristics that can result in large sustained wind-induced oscillations (WIO) on such a tall and slender launch vehicle and to generate a unique database for development and evaluation of analytical methods for predicting steady and dynamic GWL, especially those caused by vortex shedding, and resulting in significant WIO. This paper summarizes the wind-tunnel test program that employed two dynamically-aeroelastically scaled GWL models based on the Ares I-X Flight Test Vehicle. The first model tested, the GWL Checkout Model (CM), was a relatively simple model with a secondary objective of restoration and development of processes and methods for design, fabrication, testing, and data analysis of a representative ground wind loads model. In addition, parametric variations in surface roughness, Reynolds number, and protuberances (on/off) were investigated to determine effects on GWL characteristics. The second windtunnel model, the Ares I-X GWL Model, was significantly more complex and representative of the Ares I-X FTV and included the addition of simplified rigid geometrically-scaled models of the Kennedy Space Center (KSC) Mobile Launch Platform (MLP) and Launch Complex 39B primary structures. Steady and dynamic base bending moment as well as model response and steady and unsteady pressure data was acquired during the testing of both models. During wind-tunnel testing of each model, flow conditions (speed and azimuth) where significant WIO occurred, were identified and thoroughly investigated. Scaled data from the Ares I-X GWL model test was used in the determination of worst-case loads for the analysis of Ares I-X FTV design wind conditions. Finally, this paper includes a brief discussion of the limited full-scale GWL data acquired during the rollout and on-pad stay of the Ares I-X FTV that was launched from KSC on October 28, 2009.

Keller, Donald F.↗

NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗

NASA NTRS1973

Utilization of ERTS-1 data to monitor and classify eutrophication of inland lakes

The author has identified the following significant results. Bands 6 and 7 have fine structure as obtained by proper selection of digital levels in processing the CCT's. This is contrary to the imagery density received. This means that the small lakes can be classified in IR for different types of water masses. At least four distinct water masses have been determined for test lakes. They are shoreline, shallow water, and two deep waters. One deep water is patchy and presents difficulty in training set selection. The excellent weather and a completely successful field test form a significant happening. It required 12 orbits over the test area before perfect weather occurred.

Chase, P. E.↗

DOE OSTI2024

Search for X ( 1870 ) via the decay J / ψ → ω K + K − η

Using a sample of ( 10087 ± 44 ) × 10 6 J / ψ events collected by the BESIII detector at the BEPCII collider, we search for the decay X ( 1870 ) → K + K − η via the J / ψ → ω K + K − η process for the first time. No significant X ( 1870 ) signal is observed. The upper limit on the branching fraction of the decay J / ψ → ω X ( 1870 ) → ω K + K − η is determined to be 9.55 × 10 − 7 at the 90% confidence level. In addition, the branching faction B ( J / ψ → ω K + K − η ) is measured to be ( 3.33 ± 0.02 ( stat ) ± 0.12 ( syst ) ) × 10 − 4 . Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS1972

Experimental determination of the turbulence in a liquid rocket combustion chamber

The intensity of turbulence and the Lagrangian correlation coefficient for a liquid rocket combustion chamber were determined experimentally using the tracer gas diffusion method. The results indicate that the turbulent diffusion process can be adequately modeled by the one-dimensional Taylor theory; however, the numerical values show significant disagreement with previously accepted values. The intensity of turbulence is higher by a factor of about two, while the Lagrangian correlation coefficient which was assumed to be unity in the past is much less than unity.

Hara, J.↗

NASA NTRS1973

Editing ERTS-1 data to exclude land aids cluster analysis of water targets

The author has identified the following significant results. It has been determined that an increase in the number of spectrally distinct coastal water types is achieved when data values over the adjacent land areas are excluded from the processing routine. This finding resulted from an automatic clustering analysis of ERTS-1 system corrected MSS scene 1002-18134 of 25 July 1972 over Monterey Bay, California. When the entire study area data set was submitted to the clustering only two distinct water classes were extracted. However, when the land area data points were removed from the data set and resubmitted to the clustering routine, four distinct groupings of water features were identified. Additionally, unlike the previous separation, the four types could be correlated to features observable in the associated ERTS-1 imagery. This exercise demonstrates that by proper selection of data submitted to the processing routine, based upon the specific application of study, additional information may be extracted from the ERTS-1 MSS data.

Erb, R. B.↗

NASA NTRS2006

Morphing: A Novel Approach to Astronaut Suit Sizing

The fitting of a spacesuit to an astronaut is an iterative process consisting of two parts. The first uses anthropometric data to provide an approximation of the suit components that will fit the astronaut. The second part is the subjective fitting, where small adjustments are made based on the astronaut s preference. By providing a better approximation of the correct suit components, the entire fit process time can be reduced significantly. The goals of this project are twofold: (1) To evaluate the effectiveness of the existing sizing algorithm for the Mark III Hybrid suit and (2) to determine what additional components are needed in order to provide adequate sizing for the existing astronaut population. A single subject was scanned using a 3D whole-body scanner (VITUS 3D) in the Mark III suit in eight different poses and four subjects in minimal clothing were also scanned in similar poses. The 3D external body scans of the suit and the subject are overlaid and visually aligned in a customized MATLAB program. The suit components were contracted or expanded linearly along the subjects limbs to match the subjects segmental lengths. Two independent measures were obtained from the morphing program on four subjects and compared with the existing sizing information. Two of the four subjects were in correspondence with the sizing algorithm and morphing results. The morphing outcome for a third subject, incompatible with the suit, suggested that an additional arm element at least 6 inches smaller than the existing smallest suit component would need to be acquired. The morphing result of the fourth subject, deemed incompatible with the suit using the sizing algorithm, indicated a different suit configuration which would be compatible. This configuration matched with the existing suit fit check data.

Margerum, Sarah↗

NASA NTRS2003

Space Act Agreement Maker (SAAM) With Electronic Routing System (ERouter) Developed

Members of the Commercial Technology Office at the NASA Glenn Research Center have developed an exciting new tool that greatly reduces the lead time in creating and routing Space Act Agreements. The Space Act Agreement Maker (SAAM) is an e-government Web-based system that automates the initial drafting of Space Act Agreements by technical and program personnel. SAAM also is used for editing and will be used later for maintaining electronic copies of all Space Act Agreements. During the initial drafting, the software prompts NASA personnel proposing an agreement to answer questions regarding the agreement. On the basis of the answers, the software selects from a matrix of NASA standard clauses to produce a first draft of the agreement. The draft agreement and information submitted by the NASA personnel are electronically routed to Glenn s Commercial Technology Office for review and, where necessary, editing. The final version of the agreement, along with any supporting documentation, is then routed for electronic concurrence/approval to the necessary internal review participants using the electronic routing system (e-router). SAAM was developed cooperatively by Glenn s Commercial Technology Office and Glenn s Office of Chief Counsel. Currently, SAAM is being evaluated by the NASA Headquarters General Counsel Office for use at all NASA centers. This system allows for the effective processing of Space Act Agreements for NASA s internal and external customers. Document control is maintained by a database. With SAAM s electronic routing, review times can be reduced significantly, allowing Glenn to more rapidly establish partnerships with industry. Prior to the creation of SAAM, it took several hours to draft a Space Act Agreement. With SAAM in place, the document can be written in about 30 min. Using the e-router also saves time in determining where the agreement is in the routing process. The document can be tracked easily, and delays can be avoided. Important research with industry partners can commence quickly after preliminary discussions have been held. The development of these products is in line with the expanding e-government initiative that is part of the Presidential Management Agenda. By using this product, NASA researchers can secure greater support from industry and academia partners. The Space Act Agreement Maker has been very well received at NASA Headquarters and at some of the other NASA centers as well. We anticipate that the NASA Ames Research Center will have the system in place very soon, and that some of the other centers will use SAAM in the near future. The General Counsel s office at NASA Headquarters has encouraged the Glenn team to develop a similar system for processing patent licenses. Find out more about Glenn's Technology Transfer & Partnership Office http://technology.grc.nasa.gov/.

Stauber, Laurel J.↗

DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗

DOE OSTI2025

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗

DOE OSTI2024

Non-nuclear Component Signatures for Warhead Dismantlement Confirmation

The verification of warhead dismantlement is expected to be an important component in future arms reduction treaties. Historic approaches developed with future arms control treaty verification in mind often involve intrusive measurements, process monitoring, and/or inspector presence to provide confidence that an authentic warhead has been dismantled. This work explores the possibility of reducing the negative impacts of these invasive approaches while also delivering a method that is more likely to provide non-sensitive data that can be shared with not only other nuclear weapons states but also non-nuclear weapons states partners. This work explores a novel approach for verifying dispositioned non-nuclear weapon components, providing confidence post-dismantlement that a treaty accountable item that was dismantled was in fact a treaty-relevant nuclear weapon system as declared. This method provides an alternative to intrusive inspection processes in nuclear weapons production environments, which would require significant changes to the host’s operational behaviors. It achieves this by identifying intrinsic neutron-induced signatures of non-nuclear components to determine their authenticity and estimate the duration they were exposed within a nuclear weapons system using technologies that are already in use for other national security applications. Intrinsic radiation effects studies are already a part of the stockpile aging and surveillance evaluations. However, none of these technologies and approaches have been previously considered for verification applications of non-nuclear component disposition. In this report, we introduce modeling studies that have been used to identify the most promising candidate parts and materials with signatures that are measurable and actionable. These models have been validated with laboratory measurements of signatures induced by the exposure of candidate materials to neutrons over a range of times. Predictive modeling then demonstrates the methodology for estimating exposure times and/or limits. Laboratory measurements of authentic non-nuclear parts from a dismantled warhead demonstrate the feasibility of employing these signature measurements. And finally, a concept of operations (CONOPS) for the potential use of this methodology is presented.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1976

Effects of misregistration on multispectral recognition

Spatial misregistration of multispectral scanner data occurs when two or more spectral band signals supposedly representing the same location are in fact data values generated from two or more overlapping or entirely different ground locations. A study was performed at the Environmental Research Institute of Michigan to determine what effect spatial misregistration may have on the accuracy of recognition processing of agriculturally oriented scanner data. It was found that misregistration severely reduces the availability of field center pixels and introduces significant errors in the classification accuracy and correct proportion estimation of a scene containing an inflated number of mixture pixels.

Cicone, R. C.↗